检查以下代码。

scala> import org.apache.spark.sql.expressions._
scala> import org.apache.spark.sql.functions._

scala> val x = lit(30)
x: org.apache.spark.sql.Column = 30

scala> val y = lit(2)
y: org.apache.spark.sql.Column = 2

scala> df.withColumn("hop_number",(x + (row_number().over(Window.orderBy(lit(1)))-1) * y)).show(false)

+----------+
|hop_number|
+----------+
|30        |
|32        |
|34        |
|36        |
|38        |
+----------+

假设您有一个分组和排序列，您可以使用window函数。

import pyspark.sql.functions as F
from pyspark.sql.functions import udf
from pyspark.sql.types import *
from pyspark.sql import Window
tst= sqlContext.createDataFrame([(1,1,14),(1,2,4),(1,3,10),(2,1,90),(7,2,30),(2,3,11)],schema=['group','order','value'])
w=Window.partitionBy('group').orderBy('order')
tst_hop= tst.withColumn("temp",F.sum(F.lit(2)).over(w)).withColumn("hop_number",F.col('temp')+28)

结果是：

tst_hop.show()
+-----+-----+-----+----+----------+
|group|order|value|temp|hop_number|
+-----+-----+-----+----+----------+
|    1|    1|   14|   2|        30|
|    1|    2|    4|   4|        32|
|    1|    3|   10|   6|        34|
|    2|    1|   90|   2|        30|
|    2|    3|   11|   4|        32|
|    7|    2|   30|   2|        30|
+-----+-----+-----+----+----------+

如果您需要不同的方法，请提供Dataframe的示例数据。